grilops 0.1.6

GRId LOgic Puzzle Solver

grilops

a GRId LOgic Puzzle Solver library, using Python 3 and z3.

This package contains a collection of libraries and helper functions that are useful for solving and checking Nikoli-style logic puzzles using z3.

To get a feel for how to use this package to model and solve puzzles, try working through the tutorial IPython notebook, and refer to the examples and the API Documentation.

Basic Concepts and Usage

The symbols and grids modules contain the core functionality needed for modeling most puzzles. For convenience, their attributes can be accessed directly from the top-level grilops module.

Symbols represent the marks that are determined and written into a grid by a solver while solving a puzzle. For example, the symbol set of a Sudoku puzzle would be the digits 1 through 9. The symbol set of a binary determination puzzle such as Nurikabe could contain two symbols, one representing a black cell and the other representing a white cell.

A symbol grid is used to keep track of the assignment of symbols to grid cells. Generally, setting up a program to solve a puzzle using grilops involves:

• Constructing a symbol set
• Constructing a symbol grid limited to contain symbols from that symbol set
• Adding puzzle-specific constraints to cells in the symbol grid
• Checking for satisfying assignments of symbols to symbol grid cells

Grid cells are exposed as z3 constants, so built-in z3 operators can and should be used when adding puzzle-specific constraints. In addition, grilops provides several modules to help automate and abstract away the introduction of common kinds of constraints.

Loops

The grilops.loops module is helpful for adding constraints that ensure symbols connect to form closed loops. An example of a puzzle type for which this is useful is Masyu.

Regions

The grilops.regions module is helpful for adding constraints that ensure cells are grouped into orthogonally contiguous regions (polyominos) of variable shapes and sizes. Some examples of puzzle types for which this is useful are Nurikabe and Fillomino.

Shapes

The grilops.shapes module is helpful for adding constraints that ensure cells are grouped into orthogonally contiguous regions (polyominos) of fixed shapes and sizes. Some examples of puzzle types for which this is useful are Battleship and LITS.